Automated Domain Modeling with Large Language Models: A Comparative Study
Bibliographic record
Abstract
Domain modeling is an essential part of software engineering and serves as a way to represent and understand the concepts and relationships in a problem domain. Typically, software engineers interpret the problem description written in natural language and manually translate it into a domain model. Domain modeling can be time-consuming and highly depends on the expertise of software engineers. Recently, Large Language Models (LLMs) have exhibited remarkable ability in language understanding, generation, and reasoning. In this paper, we conduct a comprehensive, comparative study of using LLMs for fully automated domain modeling. We assess two powerful LLMs, GPT3.5 and GPT4, employing various prompt engineering techniques on a data set containing ten diverse domain modeling examples with reference solutions created by modeling experts. Our findings reveal that while LLMs demonstrate impressive domain understanding capabilities, they are still impractical for full automation, with the top-performing LLM achieving F1scores of 0.76 for class generation, 0.61 for attribute generation, and 0.34 for relationship generation. Moreover, the F1score is characterized by higher precision and lower recall; thus, domain elements retrieved by LLMs are often reliable, but there are many missing elements. Furthermore, modeling best practices are rarely followed in auto-generated domain models. Our data set and evaluation provide a valuable baseline for future research in automated LLM-based domain modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".